Instructions to use Elib27/qwen2.5-coder-0.5b-commit-msg-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Elib27/qwen2.5-coder-0.5b-commit-msg-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "Elib27/qwen2.5-coder-0.5b-commit-msg-lora") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct | |
| library_name: peft | |
| tags: | |
| - lora | |
| - commit-message-generation | |
| - conventional-commits | |
| - code | |
| # Qwen2.5-Coder-0.5B-Instruct — Commit Message LoRA Adapter | |
| LoRA adapter fine-tuned on top of [`Qwen/Qwen2.5-Coder-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) | |
| to generate [Conventional Commits](https://www.conventionalcommits.org/) messages from a `git diff`. | |
| Part of a project to build a local, offline `prepare-commit-msg` git hook. | |
| Full write-up: https://eliotbas.com/projects/commits-fine-tuning/ | |
| Training dataset: https://huggingface.co/datasets/Elib27/commits | |
| ## Training details | |
| - Method: LoRA (r=16, alpha=32) | |
| - Framework: Unsloth + TRL SFTTrainer | |
| - Hardware: Google Colab A100 | |
| - Dataset: ~11k (diff, commit message) pairs, see [eliotbas/commits](https://huggingface.co/datasets/Elib27/commits) | |
| See the article for full evaluation results (ROUGE-L, structural checks, LLM-as-judge) across all model sizes. | |